Bias and variance describe two different ways a learning system can fail to generalize. High bias means the learning process is too constrained to capture useful structure. High variance means its result changes too much when the training sample changes. The practical goal is not to make both quantities literally zero. It is to choose […]
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Overfitting and underfitting are two different failures of generalization. An underfit model has not learned enough useful structure to perform well, even on its training data. An overfit model performs very well on training examples but substantially worse on relevant unseen data. The goal is not perfect training performance. It is reliable performance on new
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Model evaluation asks whether a machine-learning model or AI system performs well enough for its intended use. Metrics provide evidence, but no single score can establish readiness. A useful evaluation connects the task, representative data, error costs, decision thresholds, uncertainty, failure modes, and deployment conditions. This cornerstone guide explains how to choose and interpret evidence
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Accuracy, precision, and recall describe different parts of a classification model’s performance. Accuracy measures overall correctness, precision measures how reliable positive predictions are, and recall measures how many actual positive cases the model finds. Updated: August 28, 2026 This guide compares all three metrics using the same 100-email spam-filter example. Keeping the data constant makes
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A confusion matrix shows exactly where a classification model is right and where it is wrong. Instead of reporting one overall score, it separates correct predictions from the two kinds of mistakes a model can make. Updated: August 28, 2026 This guide uses one consistent spam-filter example to explain true positives, true negatives, false positives,
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